Senior Professional, Digital Solutions

About the Role

WSP is seeking an AI Engineer to develop and support production-oriented Generative AI solutions for the Middle East AI portfolio from the Bengaluru GCC. The role will work across AI products, agents, automation workflows and enterprise integrations, with emphasis on secure engineering, measurable quality and responsible AI.

Key Responsibilities

• Develop GenAI applications, agents and automation workflows using approved designs and engineering standards.
• Implement retrieval-augmented generation, function calling, structured outputs, and prompt patterns to improve solution reliability and usability.
• Integrate AI components with enterprise systems, APIs, approved vector search services, and governed data sources.
• Build and maintain ingestion, chunking, embedding, indexing, and retrieval pipelines for structured and unstructured content.
• Create evaluation datasets and execute repeatable testing for accuracy, grounding, safety, latency, and solution quality.
• Apply responsible AI controls including validation, guardrails, content filtering, traceability, and human oversight.
• Develop clean, reusable, and well-documented Python components and participate in code reviews.
• Support deployment, monitoring, incident resolution, and continuous optimization of AI solutions.

Requirements & Tech Stack

• Working knowledge of Python, REST APIs, SQL, and software engineering fundamentals.
• Practical understanding of LLMs, embeddings, RAG, prompt engineering, and evaluation methods.
• Exposure to Azure AI services, Git-based development, CI/CD, and cloud security principles.
• Strong analytical, troubleshooting, documentation, and stakeholder communication skills.

WSP-Aligned Technology Stack

Equivalent technologies may be considered only where formally approved through WSP architecture, security, and governance processes.

• Cloud platform: Microsoft Azure
• AI services: Azure OpenAI, Azure AI Search, Azure AI Document Intelligence, Azure Machine Learning, and approved Azure AI services
• Application hosting: Azure App Service, Azure Functions, and Azure Kubernetes Service where architecturally approved
• Data and security: Azure Storage, Azure Key Vault, SQL, and approved NoSQL services
• Engineering lifecycle: Azure DevOps, Git, CI/CD, automated testing, Azure Monitor, and approved observability services
• Architecture: REST APIs, microservices, event-driven, and serverless patterns, secure API-first integration

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related discipline.
• 0–3 years of relevant software, data, or AI engineering experience, including internships or strong project experience.

Expected Outcomes

• Deliver maintainable AI components that progress from prototype through controlled production deployment.
• Improve solution quality through structured evaluation, monitoring, and iterative optimization.
• Contribute to reusable AI capabilities and patterns across the Middle East portfolio.

Preferred Qualifications

• Microsoft Azure or Azure AI certification.
• Experience with agentic AI orchestration, document intelligence, or enterprise search.
• Knowledge of MLOps, model lifecycle management, and production monitoring.
• Experience working in multi-disciplinary, agile, or globally distributed teams.

Ways of Working

• Collaborate effectively with Middle East stakeholders, GCC delivery teams, product owners, engineering specialists, and enterprise technology functions.
• Work within WSP standards for architecture, cybersecurity, data protection, responsible AI, quality assurance, and delivery governance.
• Communicate clearly, document decisions, and support transparent delivery reporting across distributed teams.